1 research outputs found
A New Algorithm using Component-wise Adaptive Trimming For Robust Mixture Regression
Mixture regression provides a statistical model for teasing out latent
heterogeneous relationships between response and independent variables. Solving
mixture regression relying on EM algorithm is highly sensitive to outliers. To
enable simultaneous outlier detection and robust parameter estimation, we
proposed a fast and efficient robust mixture regression algorithm, considering
Component-wise Adaptive Trimming (CAT). Compared with multiple existing
algorithms, it grasps a good balance of computational efficiency and
robustness, in different scenarios of simulated data, where unequal component
proportions and variances, different levels of outlier contaminations and
sample sizes, occur. The adaptive trimming ability of CAT makes it a highly
potential tool for mining the latent relationships among variables in the big
data era. CAT has been implemented in an R package 'RobMixReg' available in
CRAN